[1]} COMA is an Actor-Critic algorithm designed to alleviate the credit assignment issue in multi-agent environments. The core idea is based on Difference Rewards [2]}, which compares the current global reward of the agent to the average reward that the agent can get in the current state. COMA applies this idea to Q-functions of agents, additionally using augmented critics similar to MADDPG.
